Other

Sony Electronics Inc

Sony Corporation

Amazon Technologies, Inc.

Sont Corporation

James J Carrig

Yasuhiro Fujimori

Tetsujiro Kondo

Sugata Ghosal

Marco Paniconi

Zhourong Miao

William Knox Carey

Bradley E Marshall

Jonathan B Corley

Jeffrey Paul Ramsden

David Idris Alexander Sayed

Christopher Neil Hawes

Yasuaki Takahashi

Kohji Ohta

Tsutomu Watanabe

Hideo Nakaya

Takahiro Nagano

Tetsuiiro Kondo

Yasuhiro c/o Sony Electronics Inc Fujimori

Tetsujiro c/o Sony Electronics Inc Kondo

Tetsjiro C/0 Sony Corporation Kondo

A transcoding service is described that is capable of transcoding or otherwise processing content, such as video, audio or multimedia content, by utilizing one or more pipelines. A pipeline can enable a user to submit transcoding jobs (or other processing jobs) into an available pipeline, where a transcoding service (or other such service) assigns one or more computing resources to process the jobs received to each pipeline. The transcoding service and the pipelines can be provided by at least one service provider (e.g., a cloud computing provider) or other such entity to a plurality of customers. A service provider can also provide the computing resources (e.g., servers, virtual machines, etc.) used to process the transcoding jobs from the pipelines.

Securing content using pipelines

Temporal classified filtering encodes image data by applying filters assigned to classes of pixels in a target frame to predict values for the pixels. The pixels are classified based on their associated motion vectors and the motion vectors are used to position the filters on the reference frame. Prediction error values are also calculated. The filters, motion vectors, and prediction errors represent the pixels in the encoded image data. The reference frame may be a past or future frame of the image data, and multiple reference frames of various combinations of past and future frames may be used. The filters for multiple reference frames are three-dimensional comprising a two-dimensional filter for each reference frame. The filters may be pre-determined or generated as the frames are encoded. The image data is recreated by applying the filters to the reference frames and correcting the resulting predictions with the prediction error values.

Temporal classified filtering for video compression

A method of irregular motion compensation includes using contours of objects in a reference image to tile the reference image into a plurality of irregular shapes, and mapping each irregular shape to a location in a target image by assigning a motion vector to each irregular shape.

Natural shaped regions for motion compensation

A technique for performing motion estimation using an adaptive motion search range includes calculating motion vectors for blocks of pixels in a target image, using the calculated motion vectors to generate a search range associated with a target block of pixels in the target image, and using the generated search range to estimate motion of the target block of pixels.

Adaptive motion search range

A method of generating an adaptive temporal filter is performed by constructing a motion vector area cell around each of a plurality of motion vectors in a target image, selecting a pixel in the target image, constructing a pixel area cell around the selected pixel, determining an overlap area between the motion vector area cells and the pixel area cell, generating filter weights from the overlap area, and using the filter weights to filter the selected pixel.

Adaptive area of influence filter

A method for determining values of motion vectors includes receiving an irregular pattern of motion vectors for a target image, estimating an initial value for each of the motion vectors, using the motion vectors to generate a tap structure for an adaptive temporal prediction filter, and using the tap structure to re-estimate the value of each motion vector.

Adaptive motion estimation for temporal prediction filter over irregular motion vector samples

A method of performing adaptive temporal prediction includes receiving a target image, wherein a position of an object in the target image is different from a position of the object in a reference image, using a boundary of the object to generate an irregular pattern of target image motion vectors, using the target image motion vectors to partition the target image into area of influence cells, wherein each area of influence cell contains a unique one of the target image motion vectors, and generating a prediction of the target image by applying an adaptive area of influence filter to the area of influence cells.

Adaptive area of influence filter for moving object boundaries

A method of performing a temporal prediction includes using an irregular filter tap to produce a prediction of a target image, and applying a regular filter tap to the prediction to produce an enhanced prediction.

Classified filtering for temporal prediction

A method of filtering an image includes receiving target image data and applying a multiple stage filter to the target image data. Each stage of the filtering includes generating a motion vector sampling pattern, using the target image data and the motion vector sampling pattern to generate a temporal prediction of the target image data, and using the temporal prediction of the target image data to generate a spatial-temporal transformation of the target image data.

Multi-stage linked process for adaptive motion vector sampling in video compression

A method of creating a variable-shaped block in a target image includes generating an initial pattern of target blocks, identifying allowable combinations of target blocks, calculating a combination value for each allowable combination, using the combination value to select one of the allowable combinations, and performing the selected allowable combination to create a variable-shaped block.

Variable shape motion estimation in video sequence

A multi-level transform generates descriptions containing compressed data that represents source data using a description generation operation and variable support filters for compaction at each level. The initial level filters the source data and each subsequent level operates on data filtered by a prior level. The description assignment and filtering at each level may vary to create different operating points for the multi-level transform. Each operating point may have a corresponding error recovery process. In one aspect, an error recovery process encodes additional descriptions that are combined with non-erroneous description data to provide error recovery of the data in missing or damaged descriptions. In another aspect, a multi-level transform is created by combining description generation and variable support filters at the various levels.

Variable support robust transform for multiple description coding

A method and apparatus for nonlinear multiple motion model and moving boundary extraction are disclosed. In one embodiment, an input image is received, the input image is partitioned into regions/blocks, and the new multiple motion model is applied to each region to extract the motions and associated moving boundaries.

Growing community of inventors

San Jose, CA, United States of America

James J Carrig